Main Topic: The fundamental struggle for manufacturers to capture, consolidate, and utilize data from their physical assets – thereby hindering progress towards advanced, data-driven production.
Key Points:
- OEE as a Starting Point: Overall Equipment Efficiency (OEE) is a fundamental, widely sought-after metric for manufacturers. However, most companies are unable to effectively capture it.
- Current Data Deficiencies: Measurements are often taken manually. Established data protocols are lacking. Existing data often resides in isolated silos – if it exists at all.
- Stalled Progress: Many manufacturers are only just beginning to build fundamental OEE data collection systems. Advanced goals such as predictive maintenance, quality forecasting, and occupational safety are still a long way off.
- Cause – Assets & Infrastructure: The central obstacle is that physical assets in factories are often not designed to generate the data necessary for AI. Extensive infrastructure work is required.
- Industry Examples: Volkswagen operates 122 plants worldwide, but has never consolidated the data from them. Ford is working on establishing new digital plants for data integration. Novartis' production lines have never generated data – a widespread problem.
In short: the path to advanced, data-driven production first requires extensive foundational work on the infrastructure side – to enable consistent and comprehensive data capture from existing physical assets.